Query expansionCOMP423

Query expansion Two approaches

◦ Relevance feedback◦ Thesaurus-based

Most Slides copied from◦ http://www.cs.utexas.edu/~mooney/ir-course

Menu

Query expansion (QE) is the process of reformulating a seed query to improve retrieval performance in information retrieval operations.

Query expansion

4

Relevance Feedback• After initial retrieval results are presented,

allow the user to provide feedback on the relevance of one or more of the retrieved documents.

• Use this feedback information to reformulate the query.

• Produce new results based on reformulated query.

• Allows more interactive, multi-pass process.

5

Relevance Feedback Architecture

RankingsIR

System

Documentcorpus

RankedDocuments

1. Doc1 2. Doc2 3. Doc3 . .

1. Doc1 2. Doc2 3. Doc3 . .

Feedback

Query String

RevisedQuery

ReRankedDocuments

1. Doc2 2. Doc4 3. Doc5 . .

QueryReformulation

6

Query Reformulation

• Revise query to account for feedback:– Query Expansion: Add new terms to query

from relevant documents.– Term Reweighting: Increase weight of terms in

relevant documents and decrease weight of terms in irrelevant documents.

• Several algorithms for query reformulation.

7

Query Reformulation for VSR• Change query vector using vector

algebra.• Add the vectors for the relevant

documents to the query vector.• Subtract the vectors for the irrelevant

docs from the query vector.• This both adds both positive and

negatively weighted terms to the query as well as reweighting the initial terms.

8

Why is Feedback Not Widely Used

• Users sometimes reluctant to provide explicit feedback.

• Results in long queries that require more computation to retrieve, and search engines process lots of queries and allow little time for each one.

• Makes it harder to understand why a particular document was retrieved.

9

Pseudo Feedback

• Use relevance feedback methods without explicit user input.

• Just assume the top m retrieved documents are relevant, and use them to reformulate the query.

• Allows for query expansion that includes terms that are correlated with the query terms.

10

Pseudo Feedback Architecture

RankingsIR

System

Documentcorpus

RankedDocuments

1. Doc1 2. Doc2 3. Doc3 . .

Query String

RevisedQuery

ReRankedDocuments

1. Doc2 2. Doc4 3. Doc5 . .

QueryReformulation

1. Doc1 2. Doc2 3. Doc3 . .

PseudoFeedback

11

PseudoFeedback Results

• Found to improve performance on TREC competition ad-hoc retrieval task.

• Works even better if top documents must also satisfy additional boolean constraints in order to be used in feedback.

12

Thesaurus

• A thesaurus provides information on synonyms and semantically related words and phrases.

• Example: physician syn: ||croaker, doc, doctor, MD, medical, mediciner, medico, ||sawbones

rel: medic, general practitioner, surgeon,

13

Thesaurus-based Query Expansion

• For each term, t, in a query, expand the query with synonyms and related words of t from the thesaurus.

• May weight added terms less than original query terms.

• Generally increases recall.• May significantly decrease precision,

particularly with ambiguous terms.– “interest rate” “interest rate fascinate

evaluate”

16

WordNet Query Expansion• Add synonyms in the same synset.• Add hyponyms to add specialized terms.• Add hypernyms to generalize a query.• Add other related terms to expand query.

17

Statistical Thesaurus

• Existing human-developed thesauri are not easily available in all languages.

• Human thesuari are limited in the type and range of synonymy and semantic relations they represent.

• Semantically related terms can be discovered from statistical analysis of corpora.

19

Association Matrixw1 w2 w3 …………………..wn

w1

w2

w3

.

.wn

c11 c12 c13…………………c1n

c21

c31

.

.cn1

cij: Correlation factor between term i and term j

Dd

jkikij

k

ffc

fik : Frequency of term i in document k

20

Normalized Association Matrix

• Frequency based correlation factor favors more frequent terms.

• Normalize association scores:

• Normalized score is 1 if two terms have the same frequency in all documents.

ijjjii

ijij ccc

cs

21

Metric Correlation Matrix

• Association correlation does not account for the proximity of terms in documents, just co-occurrence frequencies within documents.

• Metric correlations account for term proximity.

iu jvVk Vk vu

ij kkrc

),(

1

Vi: Set of all occurrences of term i in any document.r(ku,kv): Distance in words between word occurrences ku and kv

( if ku and kv are occurrences in different documents).

22

Normalized Metric Correlation Matrix

• Normalize scores to account for term frequencies:

ji

ijij

VV

cs

23

Query Expansion with Correlation Matrix

• For each term i in query, expand query with the n terms, j, with the highest value of cij (sij).

• This adds semantically related terms in the “neighborhood” of the query terms.

24

Problems with Global Analysis

• Term ambiguity may introduce irrelevant statistically correlated terms.– “Apple computer” “Apple red fruit

computer”• Since terms are highly correlated

anyway, expansion may not retrieve many additional documents.

25

Automatic Local Analysis

• At query time, dynamically determine similar terms based on analysis of top-ranked retrieved documents.

• Base correlation analysis on only the “local” set of retrieved documents for a specific query.

• Avoids ambiguity by determining similar (correlated) terms only within relevant documents.– “Apple computer”

“Apple computer Powerbook laptop”

26

Global vs. Local Analysis

• Global analysis requires intensive term correlation computation only once at system development time.

• Local analysis requires intensive term correlation computation for every query at run time (although number of terms and documents is less than in global analysis).

• But local analysis gives better results.

28

Query Expansion Conclusions• Expansion of queries with related terms

can improve performance, particularly recall.

• However, must select similar terms very carefully to avoid problems, such as loss of precision.